U.S. patent application number 14/951465 was filed with the patent office on 2016-05-26 for methods and apparatus to project ratings for future broadcasts of media.
The applicant listed for this patent is The Nielsen Company (US), LLC.. Invention is credited to Jingsong Cui, Peter Campbell Doe, Scott Sereday.
Application Number | 20160150280 14/951465 |
Document ID | / |
Family ID | 56010639 |
Filed Date | 2016-05-26 |
United States Patent
Application |
20160150280 |
Kind Code |
A1 |
Cui; Jingsong ; et
al. |
May 26, 2016 |
METHODS AND APPARATUS TO PROJECT RATINGS FOR FUTURE BROADCASTS OF
MEDIA
Abstract
Methods, apparatus, systems and articles of manufacture are
disclosed to project ratings for future broadcasts of media.
Disclosed example methods include normalizing, with a processor,
audience measurement data corresponding to media exposure data,
social media exposure data and programming information associated
with a future quarter to determine normalized audience measurement
data. Disclosed example methods also include classifying a media
asset based on the programming information to determine a media
asset classification. Disclosed example methods also include
building, with the processor, a projection model based on a first
subset of the normalized audience measurement data, the first
subset of the normalized audience measurement data associated with
a first time frame relative to the future quarter, the first subset
of the normalized audience measurement data based on the media
asset classification, and applying, with the processor, the
programming information to the projection model to project ratings
for the media asset.
Inventors: |
Cui; Jingsong; (Pennington,
NJ) ; Doe; Peter Campbell; (Ridgewood, NJ) ;
Sereday; Scott; (Rochelle Park, NJ) |
|
Applicant: |
Name |
City |
State |
Country |
Type |
The Nielsen Company (US), LLC. |
New York |
NY |
US |
|
|
Family ID: |
56010639 |
Appl. No.: |
14/951465 |
Filed: |
November 24, 2015 |
Related U.S. Patent Documents
|
|
|
|
|
|
Application
Number |
Filing Date |
Patent Number |
|
|
62083716 |
Nov 24, 2014 |
|
|
|
Current U.S.
Class: |
725/14 |
Current CPC
Class: |
H04N 21/4532 20130101;
H04N 21/6582 20130101; H04N 21/252 20130101; H04N 21/251 20130101;
G06Q 50/01 20130101; G06Q 30/0201 20130101; G06Q 30/0202 20130101;
H04N 21/44222 20130101; H04N 21/4665 20130101; H04N 21/25891
20130101 |
International
Class: |
H04N 21/466 20060101
H04N021/466; H04N 21/45 20060101 H04N021/45; H04N 21/25 20060101
H04N021/25 |
Claims
1. A ratings projection method comprising: normalizing, with a
processor, audience measurement data corresponding to media
exposure data, social media exposure data and programming
information associated with a future quarter to determine
normalized audience measurement data; classifying, with the
processor, a media asset based on the programming information to
determine a media asset classification; building, with the
processor, a projection model based on a first subset of the
normalized audience measurement data, the first subset of the
normalized audience measurement data associated with a first time
frame relative to the future quarter, the first subset of the
normalized audience measurement data based on the media asset
classification; and applying, with the processor, the programming
information to the projection model to project ratings for the
media asset.
2. The method as defined in claim 1, further including classifying
the media asset as a television series when a characteristic of the
media asset is indicative of at least one of a premier episode, a
repeat episode or a new episode.
3. The method as defined in claim 2, further including, in response
to classifying the media asset as a television series, retrieving
series historical performance information related to the media
asset.
4. The method as defined in claim 1, further including classifying
the media asset as special programming when a characteristic of the
media asset is indicative of at least one of a movie or a sporting
event.
5. The method as defined in claim 1, further including excluding a
subset of the first subset of the normalized audience measurement
data based on the future quarter.
6. The method as defined in claim 5, further including selecting a
second subset of the normalized audience measurement data to train
the projection model, the second subset of the normalized audience
measurement data included in the first subset of the normalized
audience measurement data and not included in the excluded subset
of the first subset of the normalized audience measurement
data.
7. The method as defined in claim 1, wherein the first subset of
the normalized audience measurement data includes historical data
related to the media asset and related to a subset of media assets
that are (1) included in the normalized audience measurement data
and (2) related to the media asset.
8. A ratings projection comprising: a data transformer to normalize
audience measurement data corresponding to media exposure data,
social media exposure data and programming information associated
with a future quarter to determine normalized audience measurement
data; a model builder to: classify a media asset based on the
programming information to determine a media asset classification;
build a projection model based on a first subset of the normalized
audience measurement data, the first subset of the normalized
audience measurement data associated with a first time frame
relative to the future quarter, the first subset of the normalized
audience measurement data based on the media asset classification;
a ratings projector to apply the programming information to the
projection model to project ratings for the media asset.
9. The apparatus as defined in claim 8, wherein the model builder
is to classify the media asset as a television series when a
characteristic of the media asset is indicative of at least one of
a premier episode, a repeat episode or a new episode.
10. The apparatus as defined in claim 9, wherein the model builder
retrieves series historical performance information when the media
asset is classified as a television series.
11. The apparatus as defined in claim 8, wherein the model builder
is to classify the media asset as special programming when a
characteristic of the media asset is indicative of at least one of
a movie or a sporting event.
12. The apparatus as defined in claim 8, wherein the model builder
is to exclude a subset of the first subset of the normalized
audience measurement data based on the future quarter.
13. The apparatus as defined in claim 12, wherein the model builder
is to select a second subset of the normalized audience measurement
data to train the projection model, the second subset of the
normalized audience measurement data included in the first subset
of the normalized audience measurement data and not included in the
excluded subset of the first subset of the normalized audience
measurement data.
14. The apparatus as defined in claim 8, wherein the first subset
of the normalized audience measurement data includes historical
data related to the media asset and related to a subset of media
assets that are (1) included in the normalized audience measurement
data and (2) related to the media asset.
15. A tangible computer-readable storage medium comprising
instructions that, when executed, cause a processor to at least:
normalize audience measurement data corresponding to media exposure
data, social media exposure data and programming information
associated with a future quarter to determine normalized audience
measurement data; classify a media asset based on the programming
information to determine a media asset classification; build a
projection model based on a first subset of the normalized audience
measurement data, the first subset of the normalized audience
measurement data associated with a first time frame relative to the
future quarter, the first subset of the normalized audience
measurement data based on the media asset classification; and apply
the programming information to the projection model to project
ratings for the media asset.
16. The tangible machine-readable storage medium as defined in
claim 15, wherein the instructions further cause the processor to:
classify the media asset as a television series when a
characteristic of the media asset is indicative of at least one of
a premier episode, a repeat episode or a new episode; and retrieve
series historical performance information related to the media
asset when the media asset is classified as a television
series.
17. The tangible machine-readable storage medium as defined in
claim 15, wherein the instructions further cause the processor to
classify the media asset as special programming when a
characteristic of the media asset is indicative of a movie or a
sporting event.
18. The tangible machine-readable storage medium as defined in
claim 15, wherein the instructions further cause the processor to
exclude a subset of the first subset of the normalized audience
measurement data based on the future quarter.
19. The tangible machine-readable storage medium as defined in
claim 18, wherein the instructions further cause the processor to
select a second subset of the normalized audience measurement data
to train the projection model, the second subset of the normalized
audience measurement data included in the first subset of the
normalized audience measurement data and not included in the
excluded subset of the first subset of the normalized audience
measurement data.
20. The tangible machine-readable storage medium as defined in
claim 15, wherein the first subset of the normalized audience
measurement data includes historical data related to the media
asset and related to a subset of media assets that are (1) included
in the normalized audience measurement data and (2) related to the
media asset.
Description
RELATED APPLICATION
[0001] This patent claims the benefit of, and priority from, U.S.
Provisional Patent Application No. 62/083,716, filed Nov. 24, 2014,
entitled "Methods and Apparatus to Predict TV Rating Lift." U.S.
Provisional Patent Application No. 62/083,716 is hereby
incorporated by reference in its entirety.
FIELD OF THE DISCLOSURE
[0002] This disclosure relates generally to audience measurement,
and, more particularly, to methods and apparatus to project ratings
for future broadcasts of media.
BACKGROUND
[0003] Audience measurement of media (e.g., content and/or
advertisements presented by any type of medium such as television,
in theater movies, radio, Internet, etc.) is typically carried out
by monitoring media exposure of panelists that are statistically
selected to represent particular demographic groups. Audience
measurement companies, such as The Nielsen Company (US), LLC,
enroll households and persons to participate in measurement panels.
By enrolling in these measurement panels, households and persons
agree to allow the corresponding audience measurement company to
monitor their exposure to information presentations, such as media
output via a television, a radio, a computer, etc. Using various
statistical methods, the collected media exposure data is processed
to determine the size and/or demographic composition of the
audience(s) for media of interest. The audience size and/or
demographic information is valuable to, for example, advertisers,
broadcasters, content providers, manufacturers, retailers, product
developers, and/or other entities. For example, audience size and
demographic information is a factor in the placement of
advertisements, in valuing commercial time slots during a
particular program and/or generating ratings for piece(s) of
media.
BRIEF DESCRIPTION OF THE DRAWINGS
[0004] FIG. 1 illustrates an example system for audience
measurement analysis implemented in accordance with the teachings
of this disclosure to project ratings for future broadcasts of
media.
[0005] FIG. 2 is an example upfront programming schedule that may
be used by the example central facility of FIG. 1 to determine
media asset(s) for which to project ratings.
[0006] FIG. 3 is an example data table that may be used by the
example central facility of FIG. 1 to store raw data variables in
the example raw data database of FIG. 1.
[0007] FIG. 4 is an example block diagram of an example
implementation of the data transformer of FIG. 1.
[0008] FIG. 5 is an example data table that may be used by the
example data transformer of FIGS. 1 and/or 4 to transform ratings
data variables into ratings predictive features.
[0009] FIG. 6 is an example data table that may be used by the
example data transformer of FIGS. 1 and/or 4 to transform program
attributes data variables into program attributes predictive
features.
[0010] FIG. 7 is an example data table that may be used by the
example data transformer of FIGS. 1 and/or 4 to transform social
media data variables into social media predictive features.
[0011] FIG. 8 is an example data table that may be used by the
example data transformer of FIGS. 1 and/or 4 to transform spending
data variables into advertisement spending predictive features.
[0012] FIG. 9 is an example data table that may be used by the
example data transformer of FIGS. 1 and/or 4 to transform universe
estimates data variables into universe estimate predictive
features.
[0013] FIG. 10 is a flowchart representative of example
machine-readable instructions that may be executed by the example
central facility of FIG. 1 to project ratings for future broadcasts
of media.
[0014] FIG. 11 is a flowchart representative of example
machine-readable instructions that may be executed by the example
media mapper of FIG. to catalog related media.
[0015] FIG. 12 is a flowchart representative of example
machine-readable instructions that may be executed by the example
data transformer of FIGS. 1 and/or 4 to transform raw audience
measurement data to predictive features.
[0016] FIG. 13 is a flowchart representative of example
machine-readable instructions that may be executed by the example
central facility of FIG. 1 to project ratings for future broadcasts
of media.
[0017] FIG. 14 is an example schema that may be used by the example
central facility of FIG. 1 to determine predictive features
associated with a first module.
[0018] FIG. 15 is an example schema that may be used by the example
central facility of FIG. 1 to determine predictive features
associated with a second module.
[0019] FIG. 16 is an example schema that may be used by the example
central facility of FIG. 1 to determine predictive features
associated with a third module.
[0020] FIG. 17 is a flowchart representative of example
machine-readable instructions that may be executed by the example
model builder of FIG. 1 to project ratings for future broadcasts of
media.
[0021] FIG. 18 is a flowchart representative of example
machine-readable instructions that may be executed by the example
future ratings projector of FIG. 1 to project ratings for future
broadcasts of media.
[0022] FIG. 19 is a block diagram of an example processing platform
structured to execute the example machine-readable instructions of
FIGS. 10-16 and/or 17 to implement the example central facility
and/or the example data translator of FIGS. 1 and/or 4.
[0023] Wherever possible, the same reference numbers will be used
throughout the drawing(s) and accompanying written description to
refer to the same or like parts.
DETAILED DESCRIPTION
[0024] Examples disclosed herein facilitate projecting ratings for
future broadcasts of media. Disclosed examples enable estimating
television ratings for households that will tune to (or persons
that will be exposed to) a program in a future quarter. For
example, near-term projections enable estimating the television
ratings for a program that will be broadcast within two quarters of
the current quarter, while upfront projections enable estimating
the television ratings for a program that will be broadcast in
three or more quarters from the current quarter.
[0025] Exposure information (e.g., ratings) may be useful for
determining a marketing campaign and/or evaluating the
effectiveness of a marketing campaign. For example, an advertiser
who wants exposure of their asset (e.g., a product, a service,
etc.) to reach a specific audience will place advertisements in
media (e.g., a television program) whose audience represents the
characteristics of the target market. In some examples, networks
determine the cost of including an advertisement in their media
based on the ratings of the media. For example, a high rating for a
television program represents a large number of audience members
who tuned to (or were exposed to) the television program. In such
instances, the larger the audience of a television program (e.g., a
higher rating), the more networks can charge for advertisements
during the program.
[0026] In the North American television industry, an upfront is a
meeting hosted at the start of important advertising sales periods
by television network executives, attended by the press and major
advertisers. It is so named because of its main purpose, to allow
marketers to buy television commercial airtime "up front," or
several months before a television season begins. In some examples
disclosed herein, an upfront projection model is developed to
predict upfront TV ratings. For example, examples disclosed herein
include a central facility that is operated by an audience
measurement entity (AME). In some examples, the central facility
and/or the AME may collect measurement information (e.g., raw data
inputs) including historical TV ratings (e.g., NPower historical TV
ratings), social media information (e.g., information collected
from social media services such as Twitter, Google+, Facebook,
Instagram, etc.), genre information (e.g., genre data derived from
NPower genre data), sponsored-media spending (e.g., ad-spending
data provided by, for example, a media provider), etc. NPower is an
example platform of historical TV ratings developed by The Nielsen
Company (US), LLC. The NPower platform includes related
applications and tools that provide measurement of audience
measurements in the US and globally, such as National TV Toolbox.
In some examples, the central facility incorporates additional
information, such as TV brand effects (TVBE) information. TVBE is
an example metric developed by The Nielsen Company (US), LLC to
measure a TV advertisement's "breakthrough" or "resonance."
[0027] In some disclosed examples, the central facility develops
models to predict upfront TV ratings using telecast-level data. In
some such examples, the central facility generates the predictions
for each telecast. The telecast predictions may be aggregated to
provide program-level and/or network-level predictions. Separate
models may also be developed at a program level. In some examples,
the central facility mines historical database(s) to identify
programs that can be used to improve the predictions of new
programs (e.g., relevant programs). The relevancy of past programs
for predicting future programs is measured in several dimensions,
including, for example, program content, program titles, network
line-up (including day parts), etc. In some examples, historical TV
ratings have been shown to significantly improve the accuracy of
such prediction models, and, in some instances, have accounted for
an average 80% of the explanatory power of the model. The example
central facility may transform the raw data inputs (e.g.,
historical TV ratings, social media information, genre information,
sponsored-media spending information and/or TVBE, etc.) into
predictive variables/features that are used as predictors in the
predictive models. In some examples, the central facility
identifies (e.g., automatically identifies) the predictive features
among a pool of many features, as well as the most efficient
techniques and/or algorithms to utilize these features. Example
techniques and/or algorithms used by the central facility include
statistical analysis (e.g., regression models, time-series models),
data and text mining, machine learning models and/or agent-based
models. In some examples, the process of data mining and deep
learning is automated to minimize (e.g., reduce) manual/subjective
input and to reduce the amount of time required.
[0028] In some examples, the central facility processes the data
over 2-4 weeks to build the predictive models. In some such
examples, once built, the central facility applies the model over a
2-day period to predict new data. The longevity of the model (e.g.,
how often the model needs to be re-calibrated) depends on how fast
the market dynamics change. For example, the model may be
re-calibrated once-a-year.
[0029] In some examples, when historical ratings are used, the
central facility uses a gap of 1 quarter (13 weeks) when developing
the projection models. This gap, though, makes it more challenging
to achieve better accuracy, but nevertheless is desirable for
mid-term projections, such as the case of upfront projection. To
measure the developed model's performance, a mean percentage error
metric (e.g., percent (actual/forecast)--1) and/or R-sq metric
(e.g., measured between actual ratings and predicted ratings) may
be used.
[0030] In some examples, the central facility includes all program
information when developing the projection models. In some such
examples, the projection model includes only historical ratings
(e.g., information collected via the NPower platform). In some
examples, the projection model is tested using a hold-out test data
set. Hold-out test data sets were not used to train the model, and,
thus, are better suited to measure how the projection models
perform for the purpose of rating predictions.
[0031] FIG. 1 is a diagram of an example environment in which an
example system 100 constructed in accordance with the teachings of
this disclosure operates to project future ratings for media of
interest. The example system 100 of FIG. 1 includes one or more
audience measurement system(s) 105, an example client 170 and an
example central facility 125 to facilitate projecting future
ratings for media of interest in accordance with the teachings of
this disclosure. In the illustrated example of FIG. 1, the central
facility 125 estimates a percentage of a universe of TV households
(or other specified group) that will tune to a program in a future
period (e.g., in the next quarter (e.g., fiscal quarter), in three
quarters, etc.) by generating ratings projection model(s) based on,
for example, historical ratings, program characteristics, social
media indicators, advertisement spending, programming schedules,
etc.
[0032] The example system 100 of FIG. 1 includes the one or more
audience measurement system(s) 105 to collect audience measurement
data 110 from panelists and non-panelists. The example audience
measurement system(s) 105 of FIG. 1 collect panelist media
measurement data 110A via, for example, people meters operating in
statistically-selected households, set-top boxes and/or other media
devices (e.g., such as digital video recorders, personal computers,
tablet computers, smartphones, etc.) capable of monitoring and
returning monitored data for media presentations, etc. The example
panelist media measurement data 110A of FIG. 1 includes media
exposure data such as live exposure data, delayed exposure data
(e.g., relative to time-shifted viewing of media via, for example,
a digital video recorder and/or video on-demand), media performance
data, such as TV ratings (e.g., historical TV ratings), program
characteristics (e.g., attributes), such as broadcast day-of-week
information, broadcast time information, originator information
(e.g., a network or channel that broadcasts the media), genre
information, universe estimates (e.g., an estimated number of
actual households or people from which a sample will be taken and
to which data from the sample will be projected), etc. In some
examples, the panelist media measurement data 110A is associated
with demographic information (e.g., gender, age, income, etc.) of
the panelists exposed to the media.
[0033] As used herein, the term "media" includes any type of
content and/or advertisement delivered via any type of distribution
medium. Thus, media includes television programming or
advertisements, radio programming or advertisements, movies, web
sites, streaming media, etc.
[0034] Example methods, apparatus, and articles of manufacture
disclosed herein monitor media presentations at media devices. Such
media devices may include, for example, Internet-enabled
televisions, personal computers, Internet-enabled mobile handsets
(e.g., a smartphone), video game consoles (e.g., Xbox.RTM.,
PlayStation.RTM.), tablet computers (e.g., an iPad.RTM.), digital
media players (e.g., a Roku.RTM. media player, a Slingbox.RTM.,
etc.), etc. In some examples, media monitoring information is
aggregated to determine ownership and/or usage statistics of media
devices, relative rankings of usage and/or ownership of media
devices, types of uses of media devices (e.g., whether a device is
used for browsing the Internet, streaming media from the Internet,
etc.), and/or other types of media device information. In examples
disclosed herein, monitoring information includes, but is not
limited to, media identifying information (e.g., media-identifying
metadata, codes, signatures, watermarks, and/or other information
that may be used to identify presented media), application usage
information (e.g., an identifier of an application, a time and/or
duration of use of the application, a rating of the application,
etc.), and/or user-identifying information (e.g., demographic
information, a user identifier, a panelist identifier, a username,
etc.).
[0035] The example audience measurement system(s) 105 of FIG. 1
also collect social media activity data 110B related to media via,
for example, social media servers that provide social media
services to users of the social media server. As used herein, the
term social media services is defined to be a service provided to
users to enable users to share information (e.g., text, images,
data, etc.) in a virtual community and/or network. Example social
media services may include, for example, Internet forums (e.g., a
message board), blogs, micro-blogs (e.g., Twitter.RTM.), social
networks (e.g., Facebook.RTM., LinkedIn, Instagram, etc.), etc. For
example, the audience measurement system(s) 105 may monitor social
media messages communicated via social media services and identify
media-exposure social media messages (e.g., social media messages
that reference at least one media asset (e.g., media and/or a media
event)). The example audience measurement system(s) 105 may filter
the media-exposure social media messages for media-exposure social
media messages of interest (e.g., social media messages that
reference media of interest).
[0036] The example social media activity data 110B of FIG. 1
includes one or more of message identifying information (e.g., a
message identifier, a message author, etc.), timestamp information
indicative of when the social media message was posted and/or
viewed, the content of the social media message and an identifier
of the media asset referenced in the media-exposure social media
message. In some examples, the audience measurement system(s) 105
may process the media-exposure social media messages of interest
and aggregate information related to the social media messages. For
example, the audience measurement system(s) 105 may determine a
count of the media-exposure social media messages of interest, may
determine a number of unique authors who posted the media-exposure
social media messages of interest, may determine a number of
impressions of (e.g., exposure to) the media-exposure social media
messages of interest, etc.
[0037] In the illustrated example of FIG. 1, the audience
measurement system(s) 105 send the audience measurement data 110 to
the central facility 125 via an example network 115. The example
network 115 of the illustrated example of FIG. 1 is the Internet.
However, the example network 115 may be implemented using any
suitable wired and/or wireless network(s) including, for example,
one or more data buses, one or more Local Area Networks (LANs), one
or more wireless LANs, one or more cellular networks, one or more
private networks, one or more public networks, etc. The example
network 115 enables the central facility 125 to be in communication
with the audience measurement system(s) 105. As used herein, the
phrase "in communication," including variances therefore,
encompasses direct communication and/or indirect communication
through one or more intermediary components and does not require
direct physical (e.g., wired) communication and/or constant
communication, but rather includes selective communication at
periodic or aperiodic intervals, as well as one-time events.
[0038] In the illustrated example, the central facility 125 is
operated by an audience measurement entity (AME) 120 (sometimes
referred to as an "audience analytics entity" (AAE)). The example
AME 120 of the illustrated example of FIG. 1 is an entity such as
The Nielsen Company (US), LLC that monitors and/or reports exposure
to media and operates as a neutral third party. That is, in the
illustrated example, the audience measurement entity 120 does not
provide media (e.g., content and/or advertisements) to end users.
This un-involvement with the media production and/or delivery
ensures the neutral status of the audience measurement entity 120
and, thus, enhances the trusted nature of the data the AME 120
collects and processes. The reports generated by the audience
measurement entity may identify aspects of media usage, such as the
number of people who are watching television programs and
characteristics of the audiences (e.g., demographic information of
who is watching the television programs, when they are watching the
television programs, etc.).
[0039] The example AME 120 of FIG. 1 operates the central facility
125 to facilitate future projections of a media asset of interest.
As used herein, a media asset of interest is a particular media
program (e.g., identified via a program identifier such as a title,
an alphanumeric code, season and episode numbers, etc.) that is
being analyzed (e.g., for a report). In the illustrated example of
FIG. 1, the central facility 125 generates one or more reports at
the request of an example client 170 (e.g., a television network,
an advertiser, etc.). In the illustrated example, the client 170
requests projections for media of interest that will be broadcast
in the near-term (e.g., within two quarters from the current
quarter) or at a later quarter based on, for example, historical
ratings, program characteristics, social media indicators,
advertisement spending, programming schedules, etc. In the
illustrated example, the client 170 provides the AME 120 an example
programming schedule 175 that includes scheduling information for
the quarter of interest (e.g., the quarter for which the
projections are being generated). In some examples, the programming
schedule 175 indicates specific information (e.g., program
characteristics) regarding the media asset of interest such as
whether the media asset is a series (e.g., a season premier, a
repeat episode, a new episode, etc.), a special (e.g., a one-time
event such as a movie, a sporting event, etc.), etc. In some
examples, the programming schedule 175 indicates general
information, such as a program title and broadcast times of the
media. An example upfront programming schedule 200 of the
illustrated example of FIG. 2 illustrates an example programming
schedule 175 for a quarter of interest that may be provided by the
client 170.
[0040] In some examples, the client 170 may use the reports
provided by the example central facility 125 to analyze exposure to
media and take actions accordingly. For example, a television
network may increase the cost of an advertising spot (e.g.,
commercial advertising time either available for sale or purchase
from network) for media associated with relatively greater
viewership than other programs, may determine to increase the
number of episodes of the media, etc. In some examples, the client
170 (e.g., the television network) may determine whether to
discontinue producing a media program associated with relatively
lower viewership, reduce the cost of an advertising spot for media
that may be projected to have lower ratings, etc. As described
above, it is beneficial for a client (e.g., a television network)
to accurately project ratings for the media of asset since the
client may have to pay restitution to an advertiser if the
projected ratings are higher than the actual ratings and, thus, the
client charted too much for the advertisement spot. Additionally or
alternatively, the client may value an advertisement spot too low
and, thus, not maximize its gains from the media.
[0041] The central facility 125 of the illustrated example includes
a server and/or database that collects and/or receives audience
measurement data related to media assets (e.g., media and/or media
events) and projects future ratings (e.g., near-term ratings or
upfront ratings) for the media assets of interest. In some
examples, the central facility 125 is implemented using multiple
devices and/or the audience measurement system(s) 105 is (are)
implemented using multiple devices. For example, the central
facility 125 and/or the audience measurement system(s) 105 may
include disk arrays and/or multiple workstations (e.g., desktop
computers, workstation servers, laptops, etc.) in communication
with one another. In the illustrated example, the central facility
125 is in communication with the audience measurement system(s) 105
via one or more wired and/or wireless networks represented by the
network 115.
[0042] The example central facility 125 of the illustrated example
of FIG. 1 processes the audience measurement data 110 returned by
the audience measurement system(s) 105 to predict time-shifted
exposure to media. For example, the central facility 125 may
process the audience measurement data 110 to determine a
relationship between predictive features (sometimes referred to
herein as "variables," "predictors" or "factors") identified from
the audience measurement data 110 and measured ratings to build one
or more projection models. For example, the central facility 125
may generate a first projection model to project ratings for media
that will be broadcast in one or two quarters (e.g., a near-term
projection model) and/or may generate a second projection model to
project ratings for media that will be broadcast in three or more
quarters from the current quarter. The example central facility 125
may then apply data associated with the media asset of interest and
the quarter of interest to a projection model to determine a
ratings projection for the media asset.
[0043] In the illustrated example of FIG. 1, the central facility
125 includes an example data interface 130, an example raw data
database 135, an example media mapper 137, an example media catalog
139, an example data transformer 140, an example predicted features
data store 145, an example model builder 150, an example models
data store 155 and an example future ratings projector 160. In the
illustrated example of FIG. 1, the example central facility 125
includes the example data interface 130 to provide an interface
between the network 115 and the central facility 125. For example,
the data interface 130 may be a wired network interface, a wireless
network interface, a Bluetooth.RTM. network interface, etc. and may
include the associated software and/or libraries needed to
facilitate communication between the network 115 and the central
facility 125. In the illustrated example of FIG. 1, the data
interface 130 receives the audience measurement data 110 returned
by the example audience measurement system(s) 105 of FIG. 1. In the
illustrated example, the data interface 130 of FIG. 1 also receives
the programming schedule 175 provided by the client 170 of FIG. 1.
The example data interface 130 records the audience measurement
data 110 and the programming schedule 175 in the example raw data
database 135.
[0044] In the illustrated example of FIG. 1, the example central
facility 125 includes the example raw data database 135 to record
data (e.g., the example audience measurement data 110, the
programming schedule 175, etc.) provided by the audience
measurement system(s) 105 and/or the client 170 via the example
data interface 130. An example data table 200 of the illustrated
example of FIG. 2 illustrates example raw data variables that may
be recorded in the example raw data database 135. The example raw
data database 135 may be implemented by a volatile memory (e.g., a
Synchronous Dynamic Random Access Memory (SDRAM), Dynamic Random
Access Memory (DRAM), RAMBUS Dynamic Random Access Memory (RDRAM),
etc.) and/or a non-volatile memory (e.g., flash memory). The
example raw data database 135 may additionally or alternatively be
implemented by one or more double data rate (DDR) memories, such as
DDR, DDR2, DDR3, mobile DDR (mDDR), etc. The example raw data
database 135 may additionally or alternatively be implemented by
one or more mass storage devices such as hard disk drive(s),
compact disk drive(s), digital versatile disk drive(s), etc. While
in the illustrated example the raw data database 135 is illustrated
as a single database, the raw data database 135 may be implemented
by any number and/or type(s) of databases.
[0045] The example central facility 125 of the illustrated example
of FIG. 1 combines multiple disparate data sets to enable modeling
and assessment of multiple inputs simultaneously. In the
illustrated example of FIG. 1, the central facility 125 includes
the example media mapper 137 to identify and/or determine media
referencing the same media and/or media that is related. For
example, the media mapper 137 may identify a reference to a program
in the panelist media measurement data 110A by a first name (e.g.,
"How To Run A Steakhouse"), and may identify a social media message
in the social media activity data 110B referencing the same program
by a second name (e.g., "#HTRAB"). In such instances, the example
media mapper 137 maps the first name to the second name. In some
examples, the media mapper 137 may identify a third name included
in the audience measurement data 110 that includes a typographical
error in the program name (e.g., "How Too Run A Steakhouse"). In
such instances, the example media mapper 137 maps the first name,
the second name and the third name to the same program via, for
example, a media identifier (e.g., "01234").
[0046] In some examples, the media mapper 137 may determine that a
first program and a second program are not referencing the same
program, but are related to each other. For example, the second
program may be a spin-off of the first program. The example media
mapper 137 records the media mappings in the example media catalog
139. The example media mapper 137 uses title names to identify
and/or determine media referencing the same media and/or media that
is related. However, any other technique of mapping related media
may additionally or alternatively be used. For example, the media
mapper 137 may parse the raw data database 135 and identify related
media based on broadcast day and times (e.g., Tuesday, 8:00 pm),
media director(s), character name(s), actor and actress name(s),
etc.
[0047] In the illustrated example of FIG. 1, the example central
facility 125 includes the example media catalog 139 to record
mappings provided by the example media matter 137. The example
media catalog 139 may be implemented by a volatile memory (e.g., an
SDRAM, DRAM, RDRAM, etc.) and/or a non-volatile memory (e.g., flash
memory). The example media catalog 139 may additionally or
alternatively be implemented by one or more DDR memories, such as
DDR, DDR2, DDR3, mDDR, etc. The example media catalog 139 may
additionally or alternatively be implemented by one or more mass
storage devices such as hard disk drive(s), compact disk drive(s),
digital versatile disk drive(s), etc. While in the illustrated
example the media catalog 139 is illustrated as a single database,
the media catalog 139 may be implemented by any number and/or
type(s) of databases.
[0048] As described above, at least some of the variables are
transformed (e.g., modified and/or manipulated) from their raw form
in the raw data database 135 to be more meaningfully handled when
building the projection models and projecting ratings for future
broadcast(s) of media. For example, raw data may be multiplied,
aggregated, averaged, etc., and stored as predictive features
(sometimes referred to herein as "transformed," "sanitized,"
"engineered," "normalized" or "recoded" data) prior to generating
the projection models used to project the ratings for the media of
interest.
[0049] In the illustrated example of FIG. 1, the example central
facility 125 includes the example data transformer 140 to translate
the audience measurement data 110 received from the example
audience measurement system(s) 105 into a form more meaningfully
handled by the example model builder 150 (e.g., into predictive
features). For example, the data transformer 140 of FIG. 1 may
retrieve and/or query the audience measurement data 110 recorded in
the example raw data database 135 and normalize the disparate data
to a common scale. In the illustrated example, the example data
transformer 140 modifies and/or manipulates audience measurement
data 110 based on the type of data. For example, the data
transformer 140 may translate (e.g., map) data that is a string
data type (e.g., "Day-of-Week" is "Tuesday") to a Boolean data type
(e.g., "Day Tues" is set to true (e.g., "1")).
[0050] As described above and in connection with the example data
table 300 of FIG. 3, the audience measurement data 110 may be in
different data formats and/or different units of measure. For
example, program characteristic information, such as program title,
episode and season identifying information, day of week, broadcast
time, broadcast quarter, broadcast network and genre may be stored
as string data types. Current and historical ratings information
may be represented via television rating scores (e.g., floating
data types). Social media indicators (e.g., message identifiers,
message timestamps, message content, message author identifiers,
message impression information, etc.) may be represented as string
data types. In the illustrated example of FIG. 1, the data
transformer 140 normalizes the audience measurement data 110 into
numerical data types (e.g., Boolean data types, integer data types
and/or floating data types). The example data transformer 140 of
FIG. 1 records transformed data in the example predictive features
data store 145.
[0051] In the illustrated example of FIG. 1, the example central
facility 125 includes the example predictive features data store
145 to record transformed data provided by the example data
transformer 140. Example data tables 500, 600, 700, 800 and 900 of
the illustrated examples of FIGS. 5, 6, 7, 8 and 9, respectively,
illustrate example translated data variables that may be recorded
in the example predictive features data store 145. The example
predictive features data store 145 may be implemented by a volatile
memory (e.g., an SDRAM, DRAM, RDRAM, etc.) and/or a non-volatile
memory (e.g., flash memory). The example predictive features data
store 145 may additionally or alternatively be implemented by one
or more DDR memories, such as DDR, DDR2, DDR3, mDDR, etc. The
example predictive features data store 145 may additionally or
alternatively be implemented by one or more mass storage devices
such as hard disk drive(s), compact disk drive(s), digital
versatile disk drive(s), etc. While in the illustrated example the
predictive features data store 145 is illustrated as a single
database, the predictive features data store 145 may be implemented
by any number and/or type(s) of databases.
[0052] In the illustrated example of FIG. 1, the central facility
125 includes the example model builder 150 to build one or more
projection model(s) that may be used to project ratings for future
broadcast(s) of media (e.g., near-term projections, upfront
projections, etc.). In the illustrated example, the model builder
150 determines a relationship between one or more predictive
features retrieved from the example predictive features data store
145 and historical ratings to build one or more projection
model(s).
[0053] In the illustrated example of FIG. 1, the model builder 150
utilizes a Stochastic Gradient Boosting Machine (GBM) to generate
the projection models. GBM is a family of machine-learning
techniques for regression problems. In the illustrated example, the
model builder 150 produces a prediction model in the form of an
ensemble of weak prediction models, typically referred to as
"decision trees." By utilizing GBM, the example model builder 150
is able to model complex relationships, including when using
non-uniform data sources and/or missing information.
[0054] In the illustrated example of FIG. 1, the model builder 150
applies historical values of one or more predictive features from
the predictive features data store 145 to train the model using
GBM. However, any other technique may additionally or alternatively
be used to train a model. For example, the model builder 150 may
utilize an equation representative of a projection model that may
be built by the example model builder 150. In some such instances,
the model builder 150 may apply historical values of one or more
predictive features (X.sub.i) from the predictive features data
store 145 to the representative equation to train the model to
determine value of coefficients (a.sub.i) that modify the
predictive features (X.sub.i).
[0055] In the illustrated example of FIG. 1, the example model
builder 150 builds different models to project ratings for future
broadcast(s) of media based on the quarter of interest and/or
future programming information available. For example, the model
builder 150 may apply different sets of predictive features to the
GBM to determine the coefficient values (a.sub.i) of the
predicative features (X.sub.i) based on the quarter of interest
(e.g., one quarter in the future, three quarters in the future,
etc.).
[0056] In the illustrated example of FIG. 1, the example model
builder 150 selects the predictive features (X.sub.i) to apply to
GBM based on the quarter of interest and attributes of the media
assets included in the corresponding programming schedule 175. For
example, the model builder 150 may build a first projection model
by applying all available historical information (e.g., historical
ratings for media broadcast at the same time and day of week,
historical ratings for related media, etc.), social media
indicators (e.g., number of social media messages posted
referencing media of interest, number of unique authors posting
social media messages referencing media of interest, etc.), etc. to
the GBM. The example model builder 150 may build a second
projection model by applying a subset of the historical information
available to the GBM. For example, the model builder 150 may
exclude historical information equivalent to the gap of interest
(e.g., the number of quarters between the current quarter and the
quarter of interest).
[0057] In some such instances, while the general technique of GBM
is used to build projection models, the predictive features
included in the corresponding models is different and, as a result,
the ensemble of prediction models differ between the two projection
models. While the illustrated example associates near-term
projection models with one or two quarters in the future and
associates the upfront projection models with three or more
quarters in the futures, other time periods (e.g., "gaps") may
additionally or alternatively be used. The example model builder
150 of FIG. 1 stores the generated projection models in the example
models data store 155.
[0058] In the illustrated example of FIG. 1, the example central
facility 125 includes the example models data store 155 to store
projection models generated by the example model builder 150. The
example models data store 155 may be implemented by a volatile
memory (e.g., SDRAM, DRAM, RDRAM, etc.) and/or a non-volatile
memory (e.g., flash memory). The example models data store 155 may
additionally or alternatively be implemented by one or more DDR
memories, such as DDR, DDR2, DDR3, mDDR, etc. The example models
data store 155 may additionally or alternatively be implemented by
one or more mass storage devices such as hard disk drive(s),
compact disk drive(s), digital versatile disk drive(s), etc. While
in the illustrated example the models data store 155 is illustrated
as a single database, the models data store 155 may be implemented
by any number and/or type(s) of databases.
[0059] In the illustrated example of FIG. 1, the central facility
125 includes the example future ratings projector 160 to use the
projection models generated by the example model builder 150 to
project ratings for future broadcasts of media. For example, the
future ratings projector 160 may apply data related to a media
asset of interest to predict viewership of the media asset of
interest in three quarters from the current quarter. In the
illustrated example, the future ratings projector 160 uses program
characteristics of the media asset of interest and the quarter of
interest to select a projection model to apply. For example, the
future ratings projector 160 may determine a projection model based
on the gap of interest and the amount of future information
available for the media asset of interest.
[0060] In the illustrated example of FIG. 1, in response to
selecting the projection model to apply, the example future ratings
projector 160 retrieves data related to the media asset of interest
from the predictive features data store 145.
[0061] The example future ratings projector 160 of the illustrated
example of FIG. 1 applies the data related to a media asset of
interest to the generated projection models stored in the example
models data store 155 to generate reports 165 predicting the
ratings for a future broadcast of the media asset of interest. For
example, the future ratings projector 160 may estimate the ratings
for a media asset of interest by applying program attributes
information, social media indicators information and/or media
performance information to a projection model. As used herein,
program attributes information includes genre information of the
media asset of interest, media type information (e.g., a series, a
special, a repeat, a premiere, a new episode, etc.), day-of-week
information related to the media asset of interest, broadcast time
related to the media asset of interest, originator (e.g., network
or channel) information related to the media asset of interest,
etc. As used herein, social media indicators information includes a
social media messages count related to the number of media-exposure
social media messages of interest, a social media unique authors
count related to the number of unique authors who posted
media-exposure social media messages of interest, a social media
impressions count related to the number of users who were exposed
to the media-exposure social media messages of interest, etc. As
used herein, media performance information includes ratings
associated with the media asset of interest (e.g., historical
ratings associated with the media asset of interest), a day and
time of broadcast (e.g., Tuesdays at 8:00 pm), etc.
[0062] FIG. 2 is a portion of an example upfront programming
schedule 200 that may be used by the example central facility 125
of FIG. 1 to forecast ratings for media broadcast during a
corresponding future quarter. In the illustrated example, the
upfront programming schedule 200 is provided by the client 170 when
requesting the future broadcast rating projections. The example
upfront programming schedule 200 includes day of week and broadcast
times of different media including primetime media 205 and daytime
media 210. The example upfront programming schedule 200 also
includes scheduled broadcast times of special media 215.
[0063] In the illustrated example of FIG. 2, the primetime media
205 is associated with television series that run on a repeating
basis. For example, a sitcom that airs on a weekly basis is a
series. In the illustrated example, a series episode may be a
premiere episode (e.g., a first episode of a season), a new episode
(e.g., a first time that the particular episode is broadcast) or a
repeat episode. As described below, projecting the ratings for a
broadcast of a series in a future quarter is advantageous because
additional information is known. For example, historical series
performance information may be utilized when forming the
projections. In addition, future programming information is known
about the series. For example, a series that is a comedy will tend
to still be a comedy in a future quarter.
[0064] In the illustrated example of FIG. 2, the daytime media 210
is associated with little or no future programming information
availability. As described below, when media is classified as
daytime media or no future programming information is available for
the media asset, then the example central facility 125 of FIG. 1
utilizes historical program characteristics for particular days of
the week and broadcast times when projecting future ratings.
[0065] In the illustrated example of FIG. 2, the special media 215
is associated with one-time events such as movies, sporting events,
marathons (e.g., ten back-to-back episodes of a series, etc.), etc.
Similar to daytime media, special media 215 does not have past
series historical performance information. For example, in the
illustrated example of FIG. 2, the special "Life After It Exploded"
is a movie that will be broadcast two times in the fourth quarter
of 2015 (e.g., at 22:00 and then at 01:00 on Oct. 13, 2015. In such
instances, past historical ratings for the media asset (e.g., the
special "Life After It Exploded") are not available and/or are not
reliable predictors for future ratings projections. However, in the
illustrated example, the central facility 125 utilizes program
characteristics such as genre and whether the special media 215 is
a movie, a special, etc., to project future ratings.
[0066] FIG. 3 is an example data table 300 that lists raw audience
measurement data variables that the example data interface 130 of
FIG. 1 may store in the example raw data database 135 of FIG. 1. In
the illustrated example of FIG. 3, the raw audience measurement
data variables represent the data collected and/or provided by the
audience measurement system(s) 105 of FIG. 1 and/or the client 170
of FIG. 1. For example, the raw audience measurement data variables
may include the panelist media measurement data 110A collected via,
for example, people meters operating in statistically-selected
households, set-top boxes and/or other media devices (e.g., such as
digital video recorders, personal computers, tablet computers,
smartphones, etc.) capable of monitoring and returning monitored
data for media presentations, etc. The example raw audience
measurement data variables included in the data table 300 may also
include the social media activity data 110B associated with media
of interest referenced by social media messages collected via, for
example, social media servers that provide social media services to
users of the social media server. In some examples, the raw
audience measurement data may also include the programming schedule
175 and additional client-provided data, such as the amount of
money and/or resources the client anticipates spending on promoting
the media assets for the quarter.
[0067] The example data table 300 of the illustrated example of
FIG. 3 includes a variable name identifier column 305, a variable
data type identifier column 310 and a variable meaning identifier
column 315. The example variable name identifier column 305
indicates example variables that may be associated with a telecast
and/or may be useful for projecting media ratings. The example
variable data type identifier column 310 indicates a data type of
the corresponding variable. The example variable meaning identifier
column 315 provides a brief description of the value associated
with the corresponding variable. While three example variable
identifier columns are represented in the example data table 300 of
FIG. 3, more or fewer variable identifier columns may be
represented in the example data table 300. For example, the example
data table 300 may additionally or alternatively include a variable
identifier column indicative of the source of the corresponding
data (e.g., the example panelist media measurement data 110A, the
example social media activity data 110B, the client 170, etc.).
[0068] The example data table 300 of the illustrated example of
FIG. 3 includes sixteen example rows corresponding to example raw
audience measurement data variables. The example first block of
rows 350 identifies attributes and/or characteristics of a media
asset and is stored as strings. For example, the "Title" variable
identifies the name of the media asset (e.g., "Sports Stuff"), the
"Type Identifier" variable identifies the media type of the media
asset (e.g., a "series," a "movie," etc.), the "Day of Week"
variable identifies the day of the week that the media asset was
broadcast (e.g., "Tuesday"), the "Broadcast Time" variable
identifies the time during which the media asset was broadcast
(e.g., "20:00-20:30"), the "Network" variable identifies on which
network the media asset was broadcast (e.g., Channel "ABC"), and
the "Genre" variable identifies the genre that the media asset is
classified (e.g., a "comedy").
[0069] In the example data table 300 of FIG. 3, the second example
block of rows 355 identifies ratings information associated with a
media asset and the corresponding information is stored as floating
type data. For example, the "Media Ratings" variable identifies the
program ratings associated with the broadcast of the program (e.g.,
"1.01"). In the illustrated example, the ratings correspond to the
viewership during the original broadcast of the program and also
include time-shifted incremental viewing that takes place via, for
example, a DVR or video-on-demand (VOD) service during the
following 7 days (e.g., "live+7" ratings). In some examples, the
data table 300 includes ratings information for specific time and
days of the week. For example, telecast-level ratings measure
viewership at, for example, one-minute periods. In such instances,
the "DayTime ratings" variable represents the number of people who
were tuned to a particular channel at a particular minute. For
example, a first "DayTime ratings" value may represent the number
of people who were watching channel "ABC" between "20:00 and 20:01"
on "Tuesday," and a second "DayTime ratings" value may represent
the number of people who were watching channel "ABC" between "20:01
and 20:02" on "Tuesday." Although the example data table 300
includes "live+7" ratings, other ratings may additionally or
alternatively be used. For example, the ratings information in the
data table 300 may include "live" ratings, "live+same day" ratings
(e.g., ratings that represent the number of people who viewed the
media asset during its original broadcast time and/or during the
same day as the original broadcast), "C3" ratings (e.g., ratings
(sometimes presented as a percentage) that represent the number of
people who viewed a commercial spot during its original broadcast
time and/or within the following three days of the original
broadcast), "C3" impressions, etc.
[0070] In the illustrated example of FIG. 3, the example row 365
indicates the "Panelist ID" variable is stored as a string and
uniquely identifies the panelist who provided the viewership
information. For example, panelists who are provided people meters
may be assigned a panelist identifier to monitor the media exposure
of the panelist. In the illustrated example, the panelist
identifier (ID) is an obfuscated alphanumeric string to protect the
identity of the panelist. In some examples, the panelist identifier
is obfuscated in a manner so that the same obfuscated panelist
identifier information corresponds to the same panelist. In this
manner, user activities may be monitored for particular users
without exposing sensitive information regarding the panelist.
However, any other approach to protecting the privacy of a panelist
may additionally or alternatively be used. In some examples, the
panelist identifier is used to identify demographic information
associated with the panelist. For example, the panelist identifier
"0123" may link to demographic information indicating the panelist
is a male, age 19-49.
[0071] In the example data table 300 of FIG. 3, the third example
block of rows 370 identifies information regarding social media
messages. For example, the "Message ID" variable is stored as a
floating data type and is a unique identifier of a social media
message. In the illustrated example, the example "Message
Timestamp" variable is stored as a string data type and identifies
the date and/or time when the corresponding social media message
was posted. In the illustrated example, the example "Message
Content" variable is stored as a string data type and identifies
the content of corresponding social media message. In the
illustrated example, the example "Message Author" variable is
stored as a string data type and identifies the author of the
corresponding social media message.
[0072] In the example data table 300 of FIG. 3, the fourth example
block of rows 375 represents different vehicles of advertising and
represent the amount of money and/or resources that are allocated
to advertising the media asset via the corresponding vehicle. In
the illustrated example, the advertising spending amounts are
stored as floating values. Although the example data table 300
includes three different vehicles for advertisement spending, any
other number of advertising vehicles and/or vehicle types may
additionally or alternatively be used. Furthermore, in some
instances, the advertisement spending variable may not be granular
(e.g., not indicating separate vehicles), but rather represent a
total amount that the client 170 anticipates spending in
advertising for the media asset.
[0073] While sixteen example raw data variables are represented in
the example data table 300 of FIG. 3, more or fewer raw data
variables may be represented in the example data table 300
corresponding to the many raw audience measurement data variables
that may be collected and/or provided by the audience measurement
system(s) 105 of FIG. 1 and/or the client 170 of FIG. 1.
[0074] FIG. 4 is a block diagram of an example implementation of
the data transformer 140 of FIG. 1 that may facilitate manipulating
and/or modifying raw audience measurement data 110 retrieved from
the example raw data database 135. As described above, the example
data transformer 140 transforms the raw information stored in the
example raw data database 135 to a form that may be meaningfully
handled by the example model builder 150 to generate one or more
projection model(s). The example data transformer 140 of FIG. 4
includes an example ratings handler 405, an example attributes
handler 410, an example social media handler 415, an example
spending handler 420 and an example universe handler 425. In the
illustrated example, the ratings handler 405, the attributes
handler 410, the social media handler 415, the spending handler 420
and the universe handler 425 record the transformed information in
the example predictive features data store 145 of FIG. 1.
[0075] In the illustrated example of FIG. 4, the example data
transformer 140 includes the example ratings handler 405 to process
ratings-related information representative of media assets. For
example, the ratings handler 405 may query and/or retrieve
ratings-related information from the raw data database 135 (e.g.,
current ratings information, historical ratings information, etc.)
and transform the retrieved ratings-related information into a form
meaningfully handled by the example model builder 150 and/or the
example future ratings projector 160.
[0076] An example data table 500 of the illustrated example of FIG.
5 illustrates example ratings predictive features that may be
recorded by the ratings handler 405 in the example predictive
features data store 145. The example data table 500 of the
illustrated example of FIG. 5 includes a feature name identifier
column 505, a feature data type identifier column 510 and a feature
meaning identifier column 515. The example feature name identifier
column 505 indicates example predictive features that may be
associated with a media asset broadcast and/or useful for
projecting ratings for future broadcasts of the media asset. The
example feature data type identifier column 510 indicates a data
type of the corresponding predictive feature. The example feature
meaning identifier column 515 provides a brief description of the
value associated with the corresponding predictive feature. While
three example feature identifier columns are represented in the
example data table 500 of FIG. 5, more or fewer feature identifier
columns may be represented in the example data table 500.
[0077] The example data table 500 of the illustrated example of
FIG. 5 includes five example rows corresponding to example
ratings-related predictive features. The example first row 550
indicates the ratings handler 405 of FIG. 4 stores the "Hour
Ratings" feature as a floating data type. In the illustrated
example, the ratings handler 405 determines an "Hour Rating" value
based on the "DayTime Ratings" variable retrieved from the example
raw data database 135. For example, the ratings handler 405 may
query the raw data database 135 for the "DayTime Rating" values
starting at a time (e.g., "20:00") and for a day of the week
("e.g., "Tuesday"). In the illustrated example, the ratings handler
405 calculates a rating for the corresponding day and time and
records the logarithm transformation of the calculated rating as
the "Hour Rating" for an hour-long period starting at the time and
day of the week in the example predictive features data store
145.
[0078] In the illustrated example, the second example row 555
indicates the ratings handler 405 determines a "Series Ratings"
value associated with a media asset of interest based on the "Media
Ratings" variable retrieved from the example raw data database 135.
For example, the ratings handler 405 may query the raw data
database 135 for the "Media Ratings" rating related to a media
asset of interest (e.g., "Sports Stuff"). In some examples, the
media asset of interest is media included in, for example, the
example programming schedule 175 of FIG. 1. In some examples, the
media asset of interest may be media identified by the media mapper
137 as related media. In the illustrated example, the ratings
handler 405 calculates an average ratings for the media asset of
interest based on the historical ratings for the program and
records the logarithm transformation of the average ratings as the
"Series Ratings" of the media asset of interest in the example
predictive features data store 145. However, other techniques for
calculating historical ratings for a media asset (e.g., a series)
may additionally or alternative be used. For example, the ratings
handler 405 may calculate average ratings for media asset on an
episode-by-episode basis. For example, the ratings handler 405 may
retrieve all historical ratings for the second episode of Quarter 2
and calculate a media ratings value for the second episode of the
media asset.
[0079] In the illustrated example, the third example row 560
indicates the ratings handler 405 determines a "Genre Rating" value
based on the "Media Ratings" variable and the "Genre" variable
retrieved from the example raw data database 135. For example, the
ratings handler 405 may use the "Genre" variable to query the raw
data database 135 for the "Media Rating" values for media assets
classified by the genre. In the illustrated example, the ratings
handler 405 calculates an average rating for the genre and records
the logarithm transformation of the calculated average as the
"Genre Rating" in the example predictive features data store
145.
[0080] While the example data table 500 of FIG. 5 includes three
example historical ratings features, any other number of historical
ratings may additionally or alternatively be used.
[0081] In the illustrated example of FIG. 4, the example data
transformer 140 includes the example attributes handler 510 to
process attributes and/or characteristics representative of media
assets. For example, the attributes handler 510 may query and/or
retrieve program attributes information from the raw data database
135 (e.g., genre-identifying information, day-of-week information,
broadcast time-identifying information, etc.) and transform the
retrieved program attributes information into a form meaningfully
handled by the example model builder 150 and/or the example future
ratings projector 160.
[0082] An example data table 600 of the illustrated example of FIG.
6 illustrates example program attributes predictive features that
may be recorded by the attributes handler 510 in the example
predictive features data store 145. The example data table 600 of
the illustrated example of FIG. 6 includes a feature name
identifier column 605, a feature data type identifier column 610
and a feature meaning identifier column 615. The example feature
name identifier column 605 indicates example predictive features
that may be associated with a media asset and/or useful for
projecting ratings for future broadcasts of the media asset. The
example feature data type identifier column 610 indicates a data
type of the corresponding predictive feature. The example feature
meaning identifier column 615 provides a brief description of the
value associated with the corresponding predictive feature. While
three example feature identifier columns are represented in the
example data table 600 of FIG. 6, more or fewer feature identifier
columns may be represented in the example data table 600.
[0083] The example data table 600 of the illustrated example of
FIG. 6 includes fourteen example rows corresponding to example
transformed program attributes predictive features. In the
illustrated example, the example program attributes predictive
features of the data table 600 represent six example
characteristics of a media asset. The first example block of rows
650 indicates that the example attributes handler 410 stores
day-of-week information as Boolean features. In the illustrated
example, the attributes handler 410 translates day-of-week
information that is stored as a string data type at the raw data
database 135 to one or more day-of-week Boolean features. For
example, the attributes handler 410 may retrieve day-of-week
information related to a media asset indicating the date of the
week that the media asset is broadcast (e.g., "Tuesday") and set
the corresponding day-of-week Boolean feature to true (e.g., "1")
and set (or reset) other day-of-week Boolean features to false
(e.g., "0"). In the illustrated example, in response to determining
that the raw day-of-week information indicates the media asset is
broadcast on a "Tuesday," the example attributes handler 410 sets
the value of the corresponding "Day Tues" feature to true (e.g.,
"1") and sets (or resets) the values of the other day-of-week
Boolean features (e.g., "Day Mon," . . . "Day SatSun") to false
(e.g., "0"). Although the example day-of-week information is
represented as six example Boolean features in the example data
table 600 of FIG. 6, any other number of Boolean features may
additionally or alternatively be used. For example, the attributes
handler 410 may group the days-of-week information into a weekday
Boolean feature (e.g., the day-of-week is "Monday," "Tuesday,"
"Wednesday," "Thursday" or "Friday") or a weekend (e.g., the day-of
week is "Saturday" or "Sunday") Boolean feature.
[0084] The second example block of rows 655 of the data table 600
of FIG. 6 indicates that the example attributes handler 410 stores
genre-identifying information as Boolean features. In the
illustrated example, the attributes handler 410 translates
genre-identifying information that is stored as a string data type
at the raw data database 135 to one or more genre-related Boolean
features. For example, the attributes handler 410 may retrieve
genre-identifying information indicative of the genre
classification of a media asset (e.g., a documentary, drama,
variety, comedy, etc.) and set the corresponding genre-related
Boolean feature to true (e.g., "1") and set (or reset) other
genre-related Boolean features to false (e.g., "0"). In the
illustrated example, in response to determining that retrieved raw
genre-identifying information indicates the corresponding media
asset is a "comedy," the example attributes handler 410 sets the
value of the corresponding "Genre Comedy" feature to true (e.g.,
"1") and sets (or resets) the values of the other genre-related
Boolean features (e.g., "Genre Documentary," "Genre Drama" and
"Genre Variety") to false (e.g., "0"). Although the example
genre-identifying information is represented as three example
genre-related Boolean features in the example data table 600 of
FIG. 6, any other number of Boolean features representative of the
genre of a media asset may additionally or alternatively be
used.
[0085] The third example block of rows 660 of the data table 600 of
FIG. 6 indicates that the example attributes handler 410 stores
originator-identifying information as Boolean features. In the
illustrated example, the attributes handler 410 translates
originator-identifying information that is stored as a string data
type at the raw data database 135 to one or more originator-related
Boolean features. For example, the attributes handler 410 may
retrieve originator-identifying information indicative of the
network (or channel) that broadcasts a media asset (e.g., channel
"ABC," channel "XYZ," etc.) and set the corresponding
originator-related Boolean feature to true (e.g., "1") and set (or
reset) other originator-related Boolean features to false (e.g.,
"0"). In the illustrated example, in response to determining that
retrieved raw originator-identifying information indicates the
corresponding media asset is broadcast on channel "ABC," the
example attributes handler 410 sets the value of the corresponding
"Originator ABC" feature to true (e.g., "1") and sets (or resets)
the values of the other originator-related Boolean features (e.g.,
"Originator XYZ") to false (e.g., "0"). While two example
originators are represented in the example data table 600 of FIG.
6, more or fewer originators may be represented in the example data
table 600 corresponding to the many broadcast networks and cable
networks that broadcast media assets.
[0086] The example row 665 of the data table 600 of FIG. 6
indicates that the example attributes handler 410 stores broadcast
time-identifying information as an integer data type. In the
illustrated example, the attributes handler 410 maps broadcast
time-identifying information that is stored as a string data type
at the raw data database 135 to an integer. For example, the
attributes handler 410 may retrieve broadcast time-identifying
information indicative of when a media asset is broadcast (e.g.,
"00:00-01:00," "03:00-04:00," . . . "23:00-00:00") and set the
"Hour Block" feature value based on a corresponding hour block. For
example, the attributes handler 410 may map the broadcast time
"00:00-01:00" to half-hour block "0," may map the broadcast time
"01:00-02:00" to half-hour block "1," etc. Although the example
broadcast time-identifying information is represented as hour
blocks, any other granularity may additionally or alternatively be
used. For example, the broadcast times may be based on
quarter-hours, half-hours, etc.
[0087] The fourth example block of rows 660 of the data table 600
of FIG. 6 indicates that the example attributes handler 410 stores
media-type identifying information as Boolean features. In the
illustrated example, the attributes handler 410 transforms
media-type identifying information that is stored as a string data
type at the raw data database 135 to one or more media-related
Boolean features. For example, the attributes handler 410 may
retrieve media-type identifying information indicative of the
whether the media asset is a series (e.g., a program that regularly
repeats) or a special (e.g., a one-time event such as a movie, a
sporting event, a marathon of episodes, etc.) and set the
corresponding media-related Boolean feature to true (e.g., "1") and
set (or reset) other media-related Boolean features to false (e.g.,
"0"). In the illustrated example, the media-related Boolean
features identify whether the media asset is a series and a
premiere, a new or repeat episode of a series, or whether the media
asset is a special and a movie or sports event. For example, in
response to determining that retrieved raw media-type identifying
information indicates the corresponding media asset is series
premiere episode, the example attributes handler 410 sets the value
of the corresponding "Series Premiere" feature to true (e.g., "1")
and sets (or resets) the values of the other media-related Boolean
features (e.g., "Series New," "Series Repeat," "Special Movie" or
"Special Sports") to false (e.g., "0"). While three example series
media-types and two example specials media-types are represented in
the example data table 600 of FIG. 6, more or fewer originators may
be represented in the example data table 600 corresponding to the
many media types of media assets.
[0088] While the example data table 600 of FIG. 6 includes five
example program attributes related to a media asset (e.g.,
day-of-week, genre, originator and broadcast time), any other
number of program attributes may additionally or alternatively be
used.
[0089] In the illustrated example of FIG. 4, the example data
transformer 140 includes the example social media handler 415 to
process social media messages representative of media assets. For
example, the social media handler 415 may query and/or retrieve
social media messages and/or social media messages-related
information from the raw data database 135 (e.g., message
identifiers, message timestamps, message content, message authors,
etc.) and transform the retrieved social media messages and/or
related information into a form meaningfully handled by the example
model builder 150 and/or the example future ratings projector
160.
[0090] An example data table 700 of the illustrated example of FIG.
7 illustrates example social media data variables transformed into
social media predictive features that may be recorded by the social
media handler 415 in the example predictive features data store
145. The example data table 700 of the illustrated example of FIG.
7 includes a feature name identifier column 705, a feature data
type identifier column 710 and a feature meaning identifier column
715. The example feature name identifier column 705 indicates
example predictive features that may be associated with a media
asset broadcast and/or useful for projecting ratings for future
broadcasts of the media asset. The example feature data type
identifier column 710 indicates a data type of the corresponding
predictive feature. The example feature meaning identifier column
715 provides a brief description of the value associated with the
corresponding predictive feature. While three example feature
identifier columns are represented in the example data table 700 of
FIG. 7, more or fewer feature identifier columns may be represented
in the example data table 700.
[0091] The example data table 700 of the illustrated example of
FIG. 7 includes two example rows corresponding to example social
media predictive features. The first example row 750 indicates the
social media handler 415 of FIG. 4 stores an "SM Count" feature as
a floating data type in the example predictive features data store
145. In the illustrated example, the social media handler 415
determines the "SM Count" value, or social media count value,
associated with a media asset of interest based on a number of
posted social media messages of interest. For example, the social
media handler 415 may inspect the social media messages returned by
the raw data database 135 for social media messages that indicate
exposure to a media asset. For example, a media asset may be
"Sports Stuff." In such an example, a social media message of
interest may include the text "Jon is my favorite character on
Sports Stuff!" and may include a message timestamp indicating that
the social media message was posted by the message author during
broadcast of the media asset. In the illustrated example, the
social media handler 415 may count the number of social media
messages identified as of interest and record a logarithm
transformation of the number of social media messages of interest
(e.g., the social media messages that indicate exposure to a media
asset) as the "SM Count" corresponding to the media asset of
interest in the example predictive features data store 145.
[0092] The second example row 755 of the data table 700 of FIG. 7
indicates that the example social media handler 415 stores a value
related to the number of unique authors who posted social media
messages of interest as a floating data type in the example
predictive features data store 145. The second example row 755 of
the data table 700 of FIG. 7 indicates the social media handler 415
of FIG. 4 stores a "SM UAuthors" feature as a floating data type in
the example translated data database 145. In the illustrated
example, the social media handler 415 determines the "SM UAuthors"
value, or social media unique authors value, associated with a
media asset of interest based on a number of unique authors who
posted social media messages of interest. For example, the social
media handler 415 may inspect the social media messages returned by
the raw data database 135 for social media messages that indicate
exposure to a media asset. In the illustrated example, the social
media handler 415 may count the number of unique authors who posted
the social media messages identified as of interest and record a
logarithm transformation of the number of unique authors as the "SM
UAuthors" corresponding to the media asset of interest in the
example translated data database 145.
[0093] In the illustrated example, the example social media handler
415 inspects social media messages and/or social media
messages-related information retrieved from the raw data database
135 and transform(s) the retrieved social media messages and/or
related information into a form meaningfully handled by the example
model builder 150 and/or the example future ratings projector 160.
In some examples, the raw audience measurement data 110 may be
provided as aggregated data. For example, rather than providing
social media messages and/or social media messages-related
information, the example audience measurement system(s) 105 of FIG.
1 may count the number of posted social media messages related to
media assets of interest, may count the number of unique authors
who posted social media messages related to media assets of
interest, etc., and provide the respective counts to the example
central facility 125. In some such examples, the example social
media handler 415 may retrieve the respective counts and store the
logarithm transformation of the corresponding numbers as the
respective social media-related predictive features. However, any
other technique may be used to determine the number of posted
social media messages related to media assets of interest and/or
the number of unique authors who posted social media messages
related to media assets of interest.
[0094] While the example data table 700 of FIG. 7 includes two
example social media features, any other number of social media
indicators may additionally or alternatively be used. For example,
the example data table 700 may include a count of the number of
impressions associated with posted social media messages related to
media assets of interest.
[0095] In the illustrated example of FIG. 4, the example data
transformer 140 includes the example spending handler 420 to
process spending-related information representative of media
assets. For example, the spending handler 420 may query and/or
retrieve advertisement-spending variables from the raw data
database 135 and transform the retrieved advertisement-spending
variables into a form meaningfully handled by the example model
builder 150 and/or the example future ratings projector 160.
[0096] An example data table 800 of the illustrated example of FIG.
8 illustrates example advertisement-spending variables transformed
into spending predictive features that may be recorded by the
spending handler 420 in the example predictive features data store
145. The example data table 800 of the illustrated example of FIG.
8 includes a feature name identifier column 805, a feature data
type identifier column 810 and a feature meaning identifier column
815. The example feature name identifier column 805 indicates
example predictive features that may be associated with a media
asset broadcast and/or useful for projecting ratings for future
broadcasts of the media asset. The example feature data type
identifier column 810 indicates a data type of the corresponding
predictive feature. The example feature meaning identifier column
815 provides a brief description of the value associated with the
corresponding predictive feature. While three example feature
identifier columns are represented in the example data table 800 of
FIG. 8, more or fewer feature identifier columns may be represented
in the example data table 800.
[0097] The first example block of rows 855 indicates that the
example spending handler 420 stores advertisement-spending related
information as floating data types in the predictive features data
store 145. In the illustrated example, the spending handler 420
retrieves the respective amounts and store the logarithm
transformation of the corresponding amounts as the respective
advertisement-spending features. However, any other technique may
be used to determine the amount of advertisement spending
anticipated for the different advertisement vehicles. While the
example data table 800 of FIG. 8 includes seven example vehicles
for advertisement spending, any other number of advertisement
vehicles may additionally or alternatively be used.
[0098] In the illustrated example, the example row 865 indicates
that spending handler 420 determines a "Total Ad Spending" value
based on the different advertisement vehicles retrieved from the
example raw data database 135 (e.g., the fourth example block of
rows 375 of FIG. 3). For example, the spending handler 420 may
retrieve each of the different advertisement spending variables
from the raw data database 135 and sum the total amount anticipated
to be spent on advertisements for the corresponding media asset of
interest. In the illustrated example, the spending handler 420
records the logarithm transformation of the calculated total amount
as the "Total Ad Spending" feature in the example predictive
features data store 145 as a floating data type.
[0099] In the illustrated example of FIG. 4, the example data
transformer 140 includes the example universe handler 425 to
process universe estimates-related information representative of
populations for different demographic groupings. For example, the
universe handler 450 may query and/or retrieve population estimates
from the raw data database 135 and transform the retrieved
population estimates into a form meaningfully handled by the
example model builder 150 and/or the example future ratings
projector 160.
[0100] An example data table 900 of the illustrated example of FIG.
9 illustrates example population estimates variables transformed
into universe estimate features that may be recorded by the
universe handler 425 in the example predictive features data store
145. The example data table 900 of the illustrated example of FIG.
9 includes a feature name identifier column 905, a feature data
type identifier column 910 and a feature meaning identifier column
915. The example feature name identifier column 905 indicates
example predictive features that may be associated with a universe.
The example feature data type identifier column 910 indicates a
data type of the corresponding predictive feature. The example
feature meaning identifier column 915 provides a brief description
of the value associated with the corresponding predictive feature.
While three example feature identifier columns are represented in
the example data table 900 of FIG. 9, more or fewer feature
identifier columns may be represented in the example data table
900.
[0101] The example block of rows 950 of the example data table 900
indicates that the example universe handler 425 stores universe
estimates as floating data types in the predictive features data
store 145. In the illustrated example, the universe handler 425
retrieves the respective universe counts and stores the logarithm
transformation of the corresponding amounts as the respective
universe estimate features. However, any other technique may be
used to determine the estimated number of actual households or
people from which a sample is taken and to which data from the
sample will be projected. While the example data table 900 of FIG.
9 includes universe estimates for twelve example demographic
groupings, any other number of demographic groupings may
additionally or alternatively be used.
[0102] In the illustrated example, the example row 955 indicates
that the universe handler 425 determines a "Total Households" value
based on the different demographic groupings retrieved from the
example raw data database 135. For example, the universe handler
425 may retrieve each of the different universe estimate variables
from the raw data database 135 and sum the total amount households
or persons in the corresponding demographic groupings. In the
illustrated example, the universe handler 425 records the logarithm
transformation of the calculated total of households or persons as
the "Total Households" feature in the example predictive features
data store 145 as a floating data type.
[0103] While an example manner of implementing the central facility
125 of FIG. 1 is illustrated in FIG. 1, one or more of the
elements, processes and/or devices illustrated in FIG. 1 may be
combined, divided, re-arranged, omitted, eliminated and/or
implemented in any other way. Further, the example data interface
130, the example raw data database 135, the example media mapper
137, the example media catalog 139, the example data transformer
140, the example predictive features data store 145, the example
model builder 150, the example models database 155, the example
future ratings projector 160 and/or, more generally, the example
central facility 125 of FIG. 1 may be implemented by hardware,
software, firmware and/or any combination of hardware, software
and/or firmware. Thus, for example, any of the example data
interface 130, the example raw data database 135, the example media
mapper 137, the example media catalog 139, the example data
transformer 140, the example predictive features data store 145,
the example model builder 150, the example models database 155, the
example future ratings projector 160 and/or, more generally, the
example central facility 125 of FIG. 1 could be implemented by one
or more analog or digital circuit(s), logic circuits, programmable
processor(s), application specific integrated circuit(s) (ASIC(s)),
programmable logic device(s) (PLD(s)) and/or field programmable
logic device(s) (FPLD(s)). When reading any of the apparatus or
system claims of this patent to cover a purely software and/or
firmware implementation, at least one of the example data interface
130, the example raw data database 135, the example media mapper
137, the example media catalog 139, the example data transformer
140, the example predictive features data store 145, the example
model builder 150, the example models database 155, the example
future ratings projector 160 and/or, more generally, the example
central facility 125 of FIG. 1 is/are hereby expressly defined to
include a tangible computer readable storage device or storage disk
such as a memory, a digital versatile disk (DVD), a compact disk
(CD), a Blu-ray disk, etc. storing the software and/or firmware.
Further still, the example central facility 125 of FIG. 1 may
include one or more elements, processes and/or devices in addition
to, or instead of, those illustrated in FIG. 1, and/or may include
more than one of any or all of the illustrated elements, processes
and devices.
[0104] While an example manner of implementing the data transformer
140 of FIG. 1 is illustrated in FIG. 4, one or more of the
elements, processes and/or devices illustrated in FIG. 4 may be
combined, divided, re-arranged, omitted, eliminated and/or
implemented in any other way. Further, the example ratings handler
405, the example attributes handler 410, the example social media
handler 415, the example spending handler 420, the example universe
handler 425 and/or, more generally, the example data transformer
140 of FIG. 4 may be implemented by hardware, software, firmware
and/or any combination of hardware, software and/or firmware. Thus,
for example, any of the example ratings handler 405, the example
attributes handler 410, the example social media handler 415, the
example spending handler 420, the example universe handler 425
and/or, more generally, the example data transformer 140 of FIG. 4
could be implemented by one or more analog or digital circuit(s),
logic circuits, programmable processor(s), application specific
integrated circuit(s) (ASIC(s)), programmable logic device(s)
(PLD(s)) and/or field programmable logic device(s) (FPLD(s)). When
reading any of the apparatus or system claims of this patent to
cover a purely software and/or firmware implementation, at least
one of the example ratings handler 405, the example attributes
handler 410, the example social media handler 415, the example
spending handler 420, the example universe handler 425 and/or, more
generally, the example data transformer 140 of FIG. 4 is/are hereby
expressly defined to include a tangible computer readable storage
device or storage disk such as a memory, a digital versatile disk
(DVD), a compact disk (CD), a Blu-ray disk, etc. storing the
software and/or firmware. Further still, the example data
transformer 140 of FIG. 1 may include one or more elements,
processes and/or devices in addition to, or instead of, those
illustrated in FIG. 4, and/or may include more than one of any or
all of the illustrated elements, processes and devices.
[0105] Flowcharts representative of example machine readable
instructions for implementing the example central facility of FIG.
1 are shown in FIGS. 10-16 and/or 17. In these examples, the
machine readable instructions comprise a program for execution by a
processor such as the processor 1912 shown in the example processor
platform 1900 discussed below in connection with FIG. 19. The
program may be embodied in software stored on a tangible computer
readable storage medium such as a CD-ROM, a floppy disk, a hard
drive, a digital versatile disk (DVD), a Blu-ray disk, or a memory
associated with the processor 1912, but the entire program and/or
parts thereof could alternatively be executed by a device other
than the processor 1912 and/or embodied in firmware or dedicated
hardware. Further, although the example program is described with
reference to the flowcharts illustrated in FIGS. 10-16 and/or 17,
many other methods of implementing the example central facility 125
may alternatively be used. For example, the order of execution of
the blocks may be changed, and/or some of the blocks described may
be changed, eliminated, or combined.
[0106] As mentioned above, the example processes of FIGS. 10-16
and/or 17 may be implemented using coded instructions (e.g.,
computer and/or machine readable instructions) stored on a tangible
computer readable storage medium such as a hard disk drive, a flash
memory, a read-only memory (ROM), a compact disk (CD), a digital
versatile disk (DVD), a cache, a random-access memory (RAM) and/or
any other storage device or storage disk in which information is
stored for any duration (e.g., for extended time periods,
permanently, for brief instances, for temporarily buffering, and/or
for caching of the information). As used herein, the term tangible
computer readable storage medium is expressly defined to include
any type of computer readable storage device and/or storage disk
and to exclude propagating signals and to exclude transmission
media. As used herein, "tangible computer readable storage medium"
and "tangible machine readable storage medium" are used
interchangeably. Additionally or alternatively, the example
processes of FIGS. 10-16 and/or 17 may be implemented using coded
instructions (e.g., computer and/or machine readable instructions)
stored on a non-transitory computer and/or machine readable medium
such as a hard disk drive, a flash memory, a read-only memory, a
compact disk, a digital versatile disk, a cache, a random-access
memory and/or any other storage device or storage disk in which
information is stored for any duration (e.g., for extended time
periods, permanently, for brief instances, for temporarily
buffering, and/or for caching of the information). As used herein,
the term non-transitory computer readable medium is expressly
defined to include any type of computer readable storage device
and/or storage disk and to exclude propagating signals and to
exclude transmission media. As used herein, when the phrase "at
least" is used as the transition term in a preamble of a claim, it
is open-ended in the same manner as the term "comprising" is open
ended. "Comprising" and all other variants of "comprise" are
expressly defined to be open-ended terms. "Including" and all other
variants of "include" are also defined to be open-ended terms. In
contrast, the term "consisting" and/or other forms of "consist" are
defined to be close-ended terms.
[0107] FIG. 10 is a flowchart representative of example
machine-readable instructions 1000 that may be executed by the
example central facility 125 of FIG. 1 to project ratings for
future broadcasts of media. The example instructions 1000 of FIG.
10 begin at block 1002 when the example central facility 125
receives a request for ratings projections for a future broadcast
of media. For example, the client 170 may request the AME 120
project ratings for the example programming schedule 200 of FIG. 2.
The request may be to project ratings for a near-term quarter
(e.g., a quarter that is one or two quarters in the future) or a
request to project ratings for an upfront quarter (e.g., a quarter
that is three or more quarters in the future).
[0108] At block 1004, the example central facility 125 obtains data
related to the request. For example, the central facility 125 may
parse the raw data database 135 (FIG. 1) to obtain data for
building one or more projection model(s). In some examples, the
example media mapper 137 (FIG. 1) may identify media related to
media assets included in the programming schedule 200. In some
examples, the example data transformer 140 (FIGS. 1 and/or 4) may
transform raw data stored in the raw data database 135 into a form
meaningfully handled by the example model builder 150 (FIG. 1)
and/or the example future ratings projector 160 (FIG. 1).
[0109] At block 1006, the example central facility 125 builds one
or more projection model(s). For example, the model builder 150 may
determine a relationship between predictive features stored in the
predictive features data store 145 (FIG. 1) and historical ratings.
The example model builder 150 stores the generated model(s) in the
example models data store 155 (FIG. 1). An example approach to
build a projection model is described below in connection with FIG.
17.
[0110] At block 1008, the example central facility 125 determines
projected ratings for future broadcasts of media. For example, the
example future ratings projector 160 may apply data related to a
media asset of interest to a projection model to estimate ratings
for a media asset based on the programming schedule 200. An example
approach to estimate ratings for future broadcasts of media is
described below in connection with FIG. 18. The example process
1000 of FIG. 10 ends.
[0111] While in the illustrated example, the example instructions
1000 of FIG. 10 represent a single iteration of projecting ratings
for future broadcasts of media, in practice, the example
instructions 1000 of the illustrated example of FIG. 10 may be
executed in parallel (e.g., in separate threads) to allow the
central facility 125 to handle multiple requests for ratings
projections at a time.
[0112] FIG. 11 is a flowchart representative of example
machine-readable instructions 1100 that may be executed by the
example central facility 125 of FIG. 1 to catalog related media.
The example instructions 1100 of FIG. 11 begin at block 1102 when
the example central facility 125 receives audience measurement data
110 from the example audience measurement system(s) 105 of FIG. 1.
For example, the example data interface 130 (FIG. 1) may obtain
and/or retrieve example panelist media measurement data 110A and/or
example social media activity data 110B periodically and/or based
on one or more events. In some examples, the data interface 130 may
obtain and/or receive an example programming schedule 175 from the
example client 170 periodically and/or based on one or more events.
In some examples, the data interface 130 may obtain, retrieve
and/or receive the example audience measurement data 110 and/or the
programming schedule 175 aperiodically and/or as a one-time event.
The example data interface 130 stores the audience measurement data
110 in the example raw data database 135 (FIG. 1).
[0113] At block 1104, the example central facility 125 indexes the
audience measurement data 110. For example, the example media
mapper 137 (FIG. 1) may parse the raw data database 135 and
identify media identifiers associated with different media assets.
At block 1106, the example media mapper 137 identifies related
media. For example, the media mapper 137 may identify related media
by comparing program names. In some examples, the media mapper 137
may identify related media by processing the program names for
typographical errors (e.g., common typographical errors). In some
examples, the media mapper 137 utilizes title names, broadcast day
and times, media director(s), character name(s), actor and actress
name(s), etc., to identify related media.
[0114] At block 1108, the example media mapper 137 records the
media mappings. For example, the media mapper 137 may map a first
media asset (e.g., a first media asset name) to a second media
asset (e.g., a second media name) and store the media mapping in
the example media catalog 139 (FIG. 1). The example process 1100 of
FIG. 11 then ends.
[0115] FIG. 12 is a flowchart representative of example
machine-readable instructions 1200 that may be executed by the
example data transformer 140 of FIGS. 1 and/or 4 to transform raw
audience measurement data to predictive features. The example
process 1200 of the illustrated example of FIG. 12 begins at block
1202 when the example data transformer 140 obtains ratings-related
information associated with media assets. For example, the data
transformer 140 may retrieve and/or query "media" ratings,
"DayTime" ratings and/or information representative of whether a
panelist viewed a particular episode of a media asset from the
example raw data database 135. At block 1204, the example ratings
handler 405 (FIG. 4) transforms the ratings-related information to
ratings predictive features for use by the example model builder
150 and/or the example future ratings projector 160. In the
illustrated example, the ratings handler 405 transforms the
ratings-related information in accordance with the example ratings
predictive features table 500 of FIG. 5. At block 1206, the example
ratings handler 405 determines whether there is additional
ratings-related information to transform. If, at block 1206, the
ratings handler 405 determined that there is additional
ratings-related information to transform, control returns to block
1202.
[0116] If, at block 1206, the ratings handler 405 determined that
there is not additional ratings-related information to transform,
then, at block 1208, the example data transformer 140 obtains
program attributes information associated with media assets. For
example, the example attributes handler 410 (FIG. 4) may retrieve
and/or query the example raw data database 135 for day-of-week
information, genre information, network information and/or
broadcast time information. At block 1210, the example attributes
handler 410 transforms the program attributes information to
program attributes predictive features for use by the example model
builder 150 and/or the example future ratings projector 160. In the
illustrated example, the attributes handler 410 transforms the
program attributes information in accordance with the example
program attributes predictive features table 600 of FIG. 6. At
block 1212, the example attributes handler 410 determines whether
there is additional program attributes information to transform.
If, at block 1212, the attributes handler 410 determined that there
is additional program attributes information to transform, control
returns to block 1208.
[0117] If, at block 1212, the attributes handler 410 determined
that there is not additional program attributes information to
transform, then, at block 1214, the example data transformer 140
obtains social media messages-related information associated with
media assets. For example, the example social media handler 415 may
retrieve and/or query the example raw data database 135 for a
number of posted social media messages of interest and/or a number
of unique authors who posted social media messages of interest. At
block 1216, the example social media handler 415 transforms the
social media messages-related information to social media
predictive features for use by the example model builder 150 and/or
the example future ratings projector 160. In the illustrated
example, the social media handler 415 transforms the social media
messages-related information in accordance with the example social
media predictive features table 700 of FIG. 7. At block 1218, the
example social media handler 415 determines whether there is
additional social media messages-related information to transform.
If, at block 1218, the social media handler 415 determined that
there is additional social media messages-related information to
transform, control returns to block 1214.
[0118] If, at block 1218, the social media handler 415 determined
that there is not additional social media information to transform,
then, at block 1220, the example data transformer 140 obtains
spending-related information associated with media assets. For
example, the example spending handler 420 may retrieve and/or query
the example raw data database 135 for anticipated amounts (e.g., in
money or resources) associated with different advertising vehicles.
At block 1222, the example spending handler 420 transforms the
spending-related information to advertisement spending predictive
features for use by the example model builder 150 and/or the
example future ratings projector 160. In the illustrated example,
the spending handler 420 transforms the spending-related
information in accordance with the example advertisement spending
predictive features table 800 of FIG. 8. At block 1224, the example
spending handler 420 determines whether there is additional
spending-related information to transform. If, at block 1224, the
spending handler 420 determined that there is additional
spending-related information to transform, control returns to block
1220.
[0119] If, at block 1224, the spending handler 420 determined that
there is not additional spending-related information to transform,
then, at block 1226, the example data transformer 140 obtains
universe-estimates information associated with media assets. For
example, the example universe handler 425 may retrieve and/or query
the example raw data database 135 for the number of households
and/or people associated with different demographic groupings. At
block 1228, the example universe handler 425 transforms the
universe estimates-related information to universe estimates
predictive features for use by the example model builder 150 and/or
the example future ratings projector 160. In the illustrated
example, the universe handler 425 transforms the universe
estimates-related information in accordance with the example
universe estimates predictive features table 900 of FIG. 9. At
block 1230, the example universe handler 425 determines whether
there is additional universe estimates-related information to
transform. If, at block 1230, the universe handler 425 determined
that there is additional universe estimates-related information to
transform, control returns to block 1226.
[0120] If, at block 1230, the universe handler 425 determined that
there is not additional population estimates-related information to
transform, then, at block 1232, the example data transformer 140
determines whether to continue normalizing audience measurement
data. If, at block 1232, the example data transformer 140
determined to continue normalizing audience measurement data,
control returns to block 1202 to wait to obtain ratings-related
information for translating.
[0121] If, at block 1232, the example data transformer 140
determined not to continue normalizing audience measurement data,
the example process 1200 of FIG. 12 ends.
[0122] While in the illustrated example, the example instructions
1200 of FIG. 12 represent a single iteration of normalizing
audience measurement data, in practice, the example instructions
1200 of the illustrated example of FIG. 12 may be executed in
parallel (e.g., in separate threads) to allow the central facility
125 to handle multiple requests for normalizing audience
measurement data at a time.
[0123] FIG. 13 is a flowchart representative of example
machine-readable instructions 1300 that may be executed by the
example central facility 125 of FIG. 1 to project ratings for
future broadcasts of a media asset. The example process 1300 of the
illustrated example of FIG. 13 begins at block 1302 when the
example central facility 125 determines the quarter of interest.
If, at block 1302, the central facility 125 determined that the
quarter of interest is within the next two quarters (block 1304),
then, at block 1306, the central facility 125 determines to build a
near-term projection model.
[0124] If, at block 1302, the central facility 125 determined that
the quarter of interest is more than two quarters (e.g., three or
more quarters) from the current quarter (block 1308), then, at
block 1310, the central facility 125 determines to build an upfront
projection model.
[0125] At block 1312, the central facility 125 determines the
amount of future programming information that is available and
classifies the media asset accordingly. For example, if, at block
1312, the central facility 125 determined that a media asset of
interest is a television series (e.g., a regular series) (block
1314), then, at block 1316, the central facility 125 applies
predictive features associated with a first module (Module 1) when
building the projection model and predicting the future ratings for
the media asset of interest. Example predictive features associated
with Module 1 are illustrated in example schema 1400 of FIG. 14.
The example process 1300 of FIG. 13 ends.
[0126] If, at block 1312, the central facility 125 determined that
a media asset of interest is special programming (block 1318),
then, at block 1320, the central facility 125 applies predictive
features associated with a second module (Module 2) when building
the projection model and predicting the future ratings for the
media asset of interest. Example predictive features associated
with Module 2 are illustrated in example schema 1500 of FIG. 15.
The example process 1300 of FIG. 13 ends.
[0127] If, at block 1312, the central facility 125 determined that
no future programming information is available for the media asset
of intersect (block 1322), then, at block 1324, the central
facility 125 applies predictive features associated with a third
module (Module 3) when building the projection model and predicting
the future ratings for the media asset of interest. Example
predictive features associated with Module 3 are illustrated in
example schema 1600 of FIG. 16. The example process 1300 of FIG. 13
ends.
[0128] An example schema 1400 of the illustrated example of FIG. 14
illustrates example sets of predictive features that are used when
generating projection models and/or that are applied to a
projection model when projecting ratings of future broadcasts for
television series (Module 1). The example schema 1400 indicates
that audience measurement data 110 may be obtained and/or retrieved
from a National People Meter (NPM) database 1402, which includes,
but is not limited to, client-provided program characteristics,
panelist-provided demographic information and viewing behaviors of
individual households (HH) via people meters. In the illustrated
example, the data provided by the NPM database 1402 may include TV
ratings information 1404 and ratings and content characteristics
information 1406. In the illustrated example of FIG. 14, the TV
ratings information 1404 includes historical audience measurements,
such as, but not limited to, day and time ratings, series
historical performance and corresponding information for related
programs (e.g., programs related by name, day and time and/or
content and network).
[0129] In the illustrated example of FIG. 14, the ratings and
content characteristics information 1406 includes program
characteristics (e.g., genre, originator, day of week, yearly
quarter, hour block, etc.). The example ratings and content
characteristics information 1406 of FIG. 14 also includes an
indication of whether a media asset is a premier episode, a new
episode or a repeat episode. The example ratings and content
characteristics information 1406 also includes demographic
information such as household, age, gender, etc.
[0130] The example schema 1400 of FIG. 14 also includes other
client-provided information 1408 (e.g., advertisement spending),
other audience measurement system(s) information 1410 (e.g.,
universal estimates) and other third party information 1412 (e.g.,
social media indicators).
[0131] In the illustrated example, the schema 1400 indicates that
the information provided by the data sources 1402, 1404, 1406,
1408, 1410, 1412 is processed (e.g., transformed) into predictors
(e.g., predictive features). The predictors may be used by the
central facility 125 of FIG. 1 to generate projection models and/or
to project ratings of future broadcasts for television series
(Module 1).
[0132] An example schema 1500 of the illustrated example of FIG. 15
illustrates example sets of predictive features that are used when
generating projection models and/or that are applied to a
projection model when projecting ratings of future broadcasts for
special programming (Module 2). The example schema 1500 indicates
that audience measurement data 110 may be obtained and/or retrieved
from a National People Meter (NPM) database 1502, which includes,
but is not limited to, client-provided program characteristics,
panelist-provided demographic information and viewing behaviors of
individual households (HH) via people meters. In the illustrated
example, the data provided by the NPM database 1502 may include TV
ratings information 1504 and ratings and content characteristics
information 1506. In the illustrated example of FIG. 15, the TV
ratings information 1504 includes historical audience measurements,
such as, but not limited to, day and time ratings associated with a
media asset and corresponding information for related programs
(e.g., programs related by name, day and time and/or content and
network).
[0133] In the illustrated example of FIG. 15, the ratings and
content characteristics information 1506 includes program
characteristics (e.g., genre, originator, day of week, yearly
quarter, hour block, etc.). The example ratings and content
characteristics information 1506 of FIG. 15 also includes an
indication of whether a media asset is a special, a movie, etc. The
example ratings and content characteristics information 1606 of
FIG. 16 also includes an indication of whether a media asset is a
premier episode, a new episode or a repeat episode. The example
ratings and content characteristics information 1606 also includes
demographic information such as household, age, gender, etc.
[0134] The example schema 1500 of FIG. 15 also includes other
client-provided information 1508 (e.g., advertisement spending),
other audience measurement system(s) information 1510 (e.g.,
universal estimates) and other third party information 1512 (e.g.,
social media indicators).
[0135] In the illustrated example, the schema 1500 indicates that
the information provided by the data sources 1502, 1504, 1506,
1508, 1510, 1512 is processed (e.g., transformed) into predictors
(e.g., predictive features). The predictors may be used by the
central facility 125 of FIG. 1 to generate projection models and/or
to project ratings of future broadcasts for special programming
(Module 2).
[0136] An example schema 1600 of the illustrated example of FIG. 16
illustrates example sets of predictive features that are used when
generating projection models and/or that are applied to a
projection model when projecting ratings of future broadcasts for
media with unknown future programming information (Module 3). The
example schema 1600 indicates that audience measurement data 110
may be obtained and/or retrieved from a National People Meter (NPM)
database 1602, which includes, but is not limited to,
client-provided program characteristics, panelist-provided
demographic information and viewing behaviors of individual
households (HH) via people meters. In the illustrated example, the
data provided by the NPM database 1602 may include TV ratings
information 1604 and ratings and content characteristics
information 1606. In the illustrated example of FIG. 16, the TV
ratings information 1604 includes historical audience measurements,
such as, but not limited to, day and time ratings associated with a
media asset and corresponding information for related programs
(e.g., programs related by name, day and time and/or content and
network).
[0137] In the illustrated example of FIG. 16, the ratings and
content characteristics information 1606 includes program
characteristics (e.g., genre, originator, day of week, yearly
quarter, hour block, etc.). The example ratings and content
characteristics information 1606 of FIG. 16 also includes an
indication of whether a media asset is a special, a movie, a
premiere episode, a repeat episode, a new episode, etc. The example
ratings and content characteristics information 1606 of FIG. 16
also includes an indication of whether a media asset is a premier
episode, a new episode or a repeat episode. The example ratings and
content characteristics information 1606 also includes demographic
information such as household, age, gender, etc.
[0138] The example schema 1600 of FIG. 16 also includes other
client-provided information 1608 (e.g., advertisement spending),
other audience measurement system(s) information 1610 (e.g.,
universal estimates) and other third party information 1612 (e.g.,
social media indicators).
[0139] In the illustrated example, the schema 1600 indicates that
the information provided by the data sources 1602, 1604, 1606,
1608, 1610, 1612 is processed (e.g., transformed) into predictors
(e.g., predictive features). The predictors may be used by the
central facility 125 of FIG. 1 to generate projection models and/or
to project ratings of future broadcasts for media assets with
unknown future programming information (Module 3).
[0140] FIG. 17 is a flowchart representative of example
machine-readable instructions 1700 that may be executed by the
example model builder 150 of FIG. 1 to build a ratings projection
model. The example process 1700 of the illustrated example of FIG.
17 begins at block 1702 when the example model builder 150 selects
a projection model to build based on the quarter of interest. For
example, the model builder 150 may select an upfront projection
model when the quarter of interest is three or more quarters in the
future and select the near-term projection model when the quarter
of interest is one or two quarters in the future.
[0141] At block 1704, the example model builder 150 selects a
demographic grouping associated with the projection model. For
example, the model builder 150 may generate a plurality of
projection models corresponding to different demographic
segments.
[0142] At block 1706, the example model builder 150 obtains
historical data stored in the example predictive features data
store 145 (FIG. 1) based on the quarter of interest and the
selected demographic segment. In the illustrated example, the model
builder 150 obtains historical data from the predictive features
data store 145 corresponding to the eight previous quarters from
the quarter of interest. For example, if the quarter of interest is
the first quarter of 2016, then the model builder 150 retrieves
historical data from the predictive features data store 145
corresponding to the four quarters of 2015 and the four quarters of
2014.
[0143] At block 1708, the example model builder 150 determines
whether to exclude a subset of the obtained historical data based
on the selected model. For example, if, at block 1708, the model
builder 150 determined that the model builder 150 is building an
upfront projection model, then, at block 1710, the model builder
excludes historical data corresponding to the gap between the
current quarter and the quarter of interest. For example, if the
quarter of interest is three quarters in the future (e.g., Q+3),
then the gap is two quarters and historical data from the two
previous quarters (e.g., Q+1 and Q+2) is excluded when training the
model.
[0144] At block 1712, the model builder 150 generates a projection
model. For example, the model builder 150 may determine a
relationship between the included historical data and measured
ratings to generate the projection model. For example, the model
builder 150 may use any appropriate regression model, time-series
model, etc. to represent the relationship between the included
historical data and the measured ratings. In some examples, the
model builder 150 trains and validates the parameters of the
generated projection model by holding-out a subset of the data. For
example, the model builder 150 may hold-out 30% of the included
historical data and train the projection model using the remaining
70% of the historical. The model builder 150 may then use the
hold-out data to validate (e.g., test) the projection model.
[0145] At block 1714, the model builder 150 determines whether the
generated projection model satisfies a correlation threshold. For
example, if the measured error between the actual ratings and the
predicted ratings does not satisfy the correlation threshold, then
control returns to block 1712 to perform additional training and
testing iterations.
[0146] If, at block 1714, the model builder 150 determined that the
measured error does satisfy the correlation threshold, then, at
block 1716, the model builder 150 records the generated projection
model in the models data store 155.
[0147] At block 1718, the example model builder 150 determines
whether there is another demographic grouping to process for the
selected projection model. If, at 1718, the model builder 150
determined that there is another demographic grouping to process,
then control returns to block 1704 to select a demographic grouping
to process.
[0148] FIG. 18 is a flowchart representative of example
machine-readable instructions 1800 that may be executed by the
example future ratings projector 160 of FIG. 1 to project ratings
for future broadcasts of a media asset. The example process 1800 of
the illustrated example of FIG. 18 begins at block 1802 when the
example future ratings projector 160 determines whether the media
asset of interest is a television series. For example, the future
ratings projector 160 may retrieve the program characteristics of
the media asset from the predictive features data store 145 (FIG.
1). If, at block 1802, the future ratings projector 160 determined
that the media asset of interest is a television series, then, at
block 1804, the future ratings projector 160 selects Module 1 to
project the future media ratings of the media asset of interest. At
block 1806, the future ratings projector 160 obtains the predictive
features for the quarter of interest from the predictive features
data store 145 based on Module 1. In some examples, the future
ratings projector 160 may consult the example schema 1400 of the
illustrated example of FIG. 14 to determine the predicted features
associated with Module 1. Control then proceeds to block 1816 to
apply the predictive features to the media asset of interest.
[0149] If, at block 1802, the future ratings projector 160
determined that the media asset of interest is not a television
series, then, at block 1808, the future ratings projector 160
determines whether the media asset of interest is a special. For
example, the future ratings projector 160 may retrieve the program
attributes predictive features from the predictive features data
store 145. If, at block 1808, the future ratings projector 160
determined that the media asset of interest is a special, then, at
block 1810, the future ratings projector 160 selects Module 2 to
project the future media ratings of the media asset of interest. At
block 1812, the future ratings projector 160 obtains the predictive
features for the quarter of interest from the predictive features
data store 145 based on Module 2. In some examples, the future
ratings projector 160 may consult the example schema 1500 of the
illustrated example of FIG. 15 to determine the predicted features
associated with Module 2. Control then proceeds to block 1816 to
apply the predictive features to the media asset of interest.
[0150] If, at block 1808, the future ratings projector 160
determined that the media asset of interest is not a special, then,
at block 1814, the future ratings projector 160 obtains the
predictive features for the quarter of interest from the predictive
features data store 145 based on Module 3. In some examples, the
future ratings projector 160 may consult the example schema 1600 of
the illustrated example of FIG. 16 to determine the predicted
features associated with Module 3.
[0151] At block 1816, the example future ratings projector 160
applies the obtained predictive features for the quarter of
interest to the selected projection module. At block 1818, the
example program ratings estimator 160 determines whether there is
another media asset of interest to process. If, at block 1818, the
example future ratings projector 160 determined that there is
another media asset of interest to process, then control returns to
block 1802 to determine whether the media asset of interest is a
television series.
[0152] If, at block 1818, the example future ratings projector 160
determined that there is not another media asset of interest to
process, then, at block 1820, the future ratings projector 160
generates a report. For example, the future ratings projector 160
may generate a report including the projected ratings of the one or
more media asset(s) of interest. In some examples, the future
ratings projector 160 may generate a tool that can be used by
client 170 to generate the report. The example process 1800 of FIG.
18 ends.
[0153] FIG. 19 is a block diagram of an example processor platform
1900 capable of executing the instructions of FIGS. 10-16 and/or 17
to implement the central facility 125 of FIG. 1 and/or the data
transformer 140 of FIGS. 1 and/or 4. The processor platform 1900
can be, for example, a server, a personal computer, or any other
type of computing device.
[0154] The processor platform 1900 of the illustrated example
includes a processor 1912. The processor 1912 of the illustrated
example is hardware. For example, the processor 1912 can be
implemented by one or more integrated circuits, logic circuits,
microprocessors or controllers from any desired family or
manufacturer.
[0155] The processor 1912 of the illustrated example includes a
local memory 1913 (e.g., a cache). The processor 1912 of the
illustrated example executes the instructions to implement the
example data interface 130, the example media mapper 137, the
example data transformer 140, the example model builder 150, the
example future ratings projector 160, the example ratings handler
405, the example attributes handler 410, the example social media
handler 415, the example spending handler 420 and the example
universe handler 425. The processor 1912 of the illustrated example
is in communication with a main memory including a volatile memory
194 and a non-volatile memory 1916 via a bus 1918. The volatile
memory 1914 may be implemented by Synchronous Dynamic Random Access
Memory (SDRAM), Dynamic Random Access Memory (DRAM), RAMBUS Dynamic
Random Access Memory (RDRAM) and/or any other type of random access
memory device. The non-volatile memory 1916 may be implemented by
flash memory and/or any other desired type of memory device. Access
to the main memory 1914, 1916 is controlled by a memory
controller.
[0156] The processor platform 1900 of the illustrated example also
includes an interface circuit 1920. The interface circuit 1920 may
be implemented by any type of interface standard, such as an
Ethernet interface, a universal serial bus (USB), and/or a PCI
express interface.
[0157] In the illustrated example, one or more input devices 1922
are connected to the interface circuit 1920. The input device(s)
1922 permit(s) a user to enter data and commands into the processor
1912. The input device(s) can be implemented by, for example, an
audio sensor, a microphone, a camera (still or video), a keyboard,
a button, a mouse, a touchscreen, a track-pad, a trackball,
isopoint and/or a voice recognition system.
[0158] One or more output devices 1924 are also connected to the
interface circuit 1920 of the illustrated example. The output
devices 1924 can be implemented, for example, by display devices
(e.g., a light emitting diode (LED), an organic light emitting
diode (OLED), a liquid crystal display, a cathode ray tube display
(CRT), a touchscreen, a tactile output device, a printer and/or
speakers). The interface circuit 1920 of the illustrated example,
thus, typically includes a graphics driver card, a graphics driver
chip or a graphics driver processor.
[0159] The interface circuit 1920 of the illustrated example also
includes a communication device such as a transmitter, a receiver,
a transceiver, a modem and/or network interface card to facilitate
exchange of data with external machines (e.g., computing devices of
any kind) via a network 1926 (e.g., an Ethernet connection, a
digital subscriber line (DSL), a telephone line, coaxial cable, a
cellular telephone system, etc.).
[0160] The processor platform 1900 of the illustrated example also
includes one or more mass storage devices 1928 for storing software
and/or data. Examples of such mass storage devices 1928 include
floppy disk drives, hard drive disks, compact disk drives, Blu-ray
disk drives, RAID systems, and digital versatile disk (DVD) drives.
The example mass storage 1928 implements the example raw data
database 135, the example media catalog 139, the example predictive
features data store 145 and the example models data store 155.
[0161] The coded instructions 1932 of FIGS. 10-16 and/or 17 may be
stored in the mass storage device 1928, in the volatile memory
1914, in the non-volatile memory 1916, and/or on a removable
tangible computer readable storage medium such as a CD or DVD.
[0162] From the foregoing, it will appreciate that the above
disclosed methods, apparatus and articles of manufacture facilitate
projecting ratings for future broadcasts of media. For example,
disclosed examples include building a projection model based on
historical audience measurement data and future quarters of
interest. Examples disclosed herein may then apply data related to
the quarter of interest and media of interest to project ratings
for the media asset of interest.
[0163] Although certain example methods, apparatus and articles of
manufacture have been disclosed herein, the scope of coverage of
this patent is not limited thereto. On the contrary, this patent
covers all methods, apparatus and articles of manufacture fairly
falling within the scope of the claims of this patent.
* * * * *